Gene Expression Profiling For Prediction Research Articles

Effectiveness of gene expression profiling for response prediction of rectal cancer to preoperative radiotherapy

Our aim was to determine whether the expression levels of specific genes could predict clinical radiosensitivity in human colorectal cancer. Radioresistant colorectal cancer cell lines were established by repeated X-ray exposure (total, 100 Gy), and the gene expressions of the parent and radioresistant cell lines were compared in a microarray analysis. To verify the microarray data, we carried out a reverse transcriptase-polymerase chain reaction analysis of identified genes in clinical samples from 30 irradiated rectal cancer patients. A comparison of the intensity data for the parent and three radioresistant cell lines revealed 17 upregulated and 142 downregulated genes in all radioresistant cell lines. Next, we focused on two upregulated genes, PTMA (prothymosin alpha) and EIF5a2 (eukaryotic translation initiation factor 5A), in the radioresistant cell lines. In clinical samples, the expression of PTMA was significantly higher in the minor effect group than in the major effect group (P = 0.004), but there were no significant differences in EIF5a2 expression between the two groups. We identified radiation-related genes in colorectal cancer and demonstrated that PTMA may play an important role in radiosensitivity. Our findings suggest that PTMA may be a novel marker for predicting the effectiveness of radiotherapy in clinical cases.

Gene expression profiling for prediction of distant metastasis and survival in primary nasopharyngeal carcinoma

5503 Background: Distant metastasis is the major cause of treatment failure for nasopharyngeal carcinoma (NPC). An accurate means to predict potential of distant metastasis before therapy could allow optimization and further improvement of treatment. Gene expression profiling was performed to identify such predictors. Methods: mRNA transcript profiling was performed using Affymetrix U133A GeneChips on 138 biopsy specimens of primary NPC obtained before therapy and stored in liquid nitrogen. The specimens represented two groups of patients: distant metastasis <3 years after initial therapy (high risk group, n = 47) vs no distant metastasis after >3 years of follow-up (low risk group, n = 91). Two thirds of cases in each risk group were randomized to a training set and 1/3 to an independent test set. Supervised analyses were conducted to discover gene expression signatures associated with metastasis in the training set (n = 96). Genes were selected by Kruskal-Wallis test, grouped by self-organizing-map and further selected by logistic regression. Predictive models were established using logistic regression and k-nearest neighbors (kNN). The predictors thus discovered were validated using independent test set (n = 42). Results: Two molecular predictors were established for identification of patients with high or low risk of developing distant metastasis. The first predictor was based on 52 genes and kNN. The second predictor was based on 12 genes and logistic regression. The specificity, the sensitivity and the accuracy determined on the independent test set were 86%, 69% and 81% for the 52-genes predictor, and 72%, 85% and 77% for the 12-genes predictor. The accuracy increased to 85% and the estimated hazard ratio between predicted high and low risk was 11.1 (95% CI=2.4 to 52.4, p = 0.002), when both molecular predictors were used in combination. Log rank test showed significant difference in overall survival between patients predicted with low and high risk for distant metastasis in the test set (p = 0.003). Conclusions: mRNA transcript profiling of primary tumors of NPC can be used to determine the risk of distant metastasis. The method can be used to identify NPC patients at high metastatic risk to undergo trials of new modalities of treatment at the time of diagnosis. No significant financial relationships to disclose.

P67. Pretherapeutical gene expression profiling for response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy and its impact on disease free survival

P67. Pretherapeutical gene expression profiling for response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy and its impact on disease free survival

Effectiveness of Gene Expression Profiling for Response Prediction of Rectal Adenocarcinomas to Preoperative Chemoradiotherapy

There is a wide spectrum of tumor responsiveness of rectal adenocarcinomas to preoperative chemoradiotherapy ranging from complete response to complete resistance. This study aimed to investigate whether parallel gene expression profiling of the primary tumor can contribute to stratification of patients into groups of responders or nonresponders. Pretherapeutic biopsies from 30 locally advanced rectal carcinomas were analyzed for gene expression signatures using microarrays. All patients were participants of a phase III clinical trial (CAO/ARO/AIO-94, German Rectal Cancer Trial) and were randomized to receive a preoperative combined-modality therapy including fluorouracil and radiation. Class comparison was used to identify a set of genes that were differentially expressed between responders and nonresponders as measured by T level downsizing and histopathologic tumor regression grading. In an initial set of 23 patients, responders and nonresponders showed significantly different expression levels for 54 genes (P < .001). The ability to predict response to therapy using gene expression profiles was rigorously evaluated using leave-one-out cross-validation. Tumor behavior was correctly predicted in 83% of patients (P = .02). Sensitivity (correct prediction of response) was 78%, and specificity (correct prediction of nonresponse) was 86%, with a positive and negative predictive value of 78% and 86%, respectively. Our results suggest that pretherapeutic gene expression profiling may assist in response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. The implementation of gene expression profiles for treatment stratification and clinical management of cancer patients requires validation in large, independent studies, which are now warranted.

Gene expression profiling for prediction of treatment response

9594 Background: We designed a strategy for patient stratification aimed at identifying tumor gene expression profiles that predict a favorable response to chemotherapy and allow selection of patients who would most benefit from treatment. Methods: A statistical model was developed based on global gene expression analysis of samples from 119 individuals. Two approaches were compared for their ability to disclose predictive gene expression profiles: (1) Compare normal ovary and ovarian adenocarcinoma and test that gene profile as a marker set to distinguish responsive from similarly treated, non-responsive carcinomas or (2) Directly compare gene expression profiles of responders and non-responders and apply this profile to patient samples to predict outcome. Samples were involved tissues from patients with ovarian cystadenocarcinoma obtained prior to treatment (n = 26) and histologically normal ovaries from controls (n = 93). 85% (22/26) of the ovarian carcinoma patients were advanced (Stage III-IV) disease and all were treated with paclitaxel-carboplatin. Follow-up was 6–59 months with an overall response rate of 61.5%. Gene expression was determined using Affymetrix U133 microarrays that contain >40,000 known genes and ESTs. Marker profiles were sought from pair-wise comparisons and successive validation performed by drop-replace analysis within the training sets. Results: Gene expression profiles were identified from the normal-disease comparison and were predictive of patient response to treatment. Genes can be individually weighted for their value in predicting treatment response and potentially chosen and assembled into a prognostic marker set. Conclusion: Cross-validation of the training set of 26 ovarian cancers, gives preliminary evidence that a gene set predicts outcome. The reference database of normal samples adds dimension to the building of classification models for patient disease stratification. This approach can now be compared to the predictive value of within-disease gene sets derived in the absence of normal samples. Gene expression promises to be valuable in selecting patients for clinical trials and disease management. Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Gene Logic, Inc. Gene Logic, Inc. Gene Logic, Inc.

Gene expression profiling for prediction of treatment response

9594 Background: We designed a strategy for patient stratification aimed at identifying tumor gene expression profiles that predict a favorable response to chemotherapy and allow selection of patients who would most benefit from treatment. Methods: A statistical model was developed based on global gene expression analysis of samples from 119 individuals. Two approaches were compared for their ability to disclose predictive gene expression profiles: (1) Compare normal ovary and ovarian adenocarcinoma and test that gene profile as a marker set to distinguish responsive from similarly treated, non-responsive carcinomas or (2) Directly compare gene expression profiles of responders and non-responders and apply this profile to patient samples to predict outcome. Samples were involved tissues from patients with ovarian cystadenocarcinoma obtained prior to treatment (n = 26) and histologically normal ovaries from controls (n = 93). 85% (22/26) of the ovarian carcinoma patients were advanced (Stage III-IV) disease and all were treated with paclitaxel-carboplatin. Follow-up was 6–59 months with an overall response rate of 61.5%. Gene expression was determined using Affymetrix U133 microarrays that contain >40,000 known genes and ESTs. Marker profiles were sought from pair-wise comparisons and successive validation performed by drop-replace analysis within the training sets. Results: Gene expression profiles were identified from the normal-disease comparison and were predictive of patient response to treatment. Genes can be individually weighted for their value in predicting treatment response and potentially chosen and assembled into a prognostic marker set. Conclusion: Cross-validation of the training set of 26 ovarian cancers, gives preliminary evidence that a gene set predicts outcome. The reference database of normal samples adds dimension to the building of classification models for patient disease stratification. This approach can now be compared to the predictive value of within-disease gene sets derived in the absence of normal samples. Gene expression promises to be valuable in selecting patients for clinical trials and disease management. Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Gene Logic, Inc. Gene Logic, Inc. Gene Logic, Inc.

Gene expression profiling (GEP) for the prediction of response to neoadjuvant paclitaxel and gemcitabine in breast cancer (BC). Preliminary results from a Phase II trial

580 Background: GEP of primary breast tumors can be useful in selecting specific chemotherapy regimens based on their probabilities of response. Methods: A neoadjuvant phase II trial for stage II-III BC patients was prospectively designed to test the ability of GEP to predict clinical and pathological responses. Treatment schedule consisted of 6 cycles of both G 2,500 mg/m2 and T 150 mg/m2 on day 1 of a 14 day-cycle before surgery. All 70 patients are already recruited. Mean age 50 yr (range, 26–71), and tumor size: 35 mm (range; 23–100). The analysis of GEP was performed with amplified-RNA from frozen tru-cut biopsies hybridized against a Universal Human Reference RNA pool. GEP analysis was performed in duplicate in 70% of the samples with a correlation coefficient of 0.95 (CNIO Oncochip is a cDNA array comprising 9,300 cancer-related genes: http://bioinfo.cnio.es/data/oncochip/). The analysis of predictor genes was performed using the Random Forests method. To validate the genes in the predictor gene se...

Host genetics and tumour metastasis.

Metastasis, the spread and growth of tumours at secondary sites, is an extremely important clinical event, since a majority of cancer mortality is associated with the metastatic tumours, rather than the primary tumour. In spite of the importance of metastasis in the clinical setting, the actual process is extremely inefficient. Millions of tumour cells can be shed into the vasculature daily; yet, few secondary tumours are formed. The classical hypothesis explaining the inefficiency was a series of secondary events occurring in the tumour, resulting in a small subpopulation of cells capable of completing all of the steps required to successfully colonise a distant site. However, recent discoveries demonstrating the ability to predict metastatic propensity from gene expression profiles in bulk tumour tissue are not consistent with only a small subpopulation of cells in the primary tumour acquiring metastatic ability, suggesting that metastatic ability might be pre-programmed in tumours by the initiating oncogenic mutations. Data supporting both of these seemingly incompatible theories exist. Therefore, to reconcile the observed results, additional variables need to be added to the model of metastatic inefficiency. One possible variable that might explain the discrepancies is genetic background effects. Studies have demonstrated that the genetic background on which a tumour arises on can have significant affects on the ability of the tumour to metastasise and on gene expression profiles. Thus, the observations could be reconciled by combining the theories, with genetic background influencing both metastatic efficiency and predictive gene expression profiles, upon which, subsequently, metastasis-promoting mutational and epigenetic events occur. If the genetic background is an important determinant of metastatic efficiency, it would have significant implications for the clinical prediction and treatment of metastatic disease, as well as for the design of potential prevention strategies.

Allelic diversity in the host genetic background may be an important determinant in tumor metastatic dissemination.

Metastasis, the spread and growth of tumors at secondary sites, is an extremely important clinical event, since the majority of cancer mortality is associated with the metastatic tumors rather than the primary tumor. In spite of the importance of metastasis in the clinical setting, the actual process is extremely inefficient. Millions of tumor cells can be shed into the vasculature daily yet few secondary tumors are formed. To successfully colonize a distant site tumor cells must overcome a series of barriers. Failure to complete any single step in the metastatic cascade abrogates the ability to form a secondary lesion. A variety of theories have been proposed to explain the inefficiency of the metastatic process. The most commonly accepted, the progression theory, posits a series of random mutational occurs within a primary tumor to generate a small subpopulation that acquires full metastatic capability. While significant evidence supports this model, recent discoveries demonstrating the ability to predict metastatic propensity from gene expression profiles in bulk tumor tissue are not consistent with only a small subpopulation of cells in the primary tumor acquiring metastatic ability. A second theory of metastatic inefficiency, the transient compartment theory, is more consistent with the microarray data, but does not completely explain observations like metastasis associated loss-of-heterozygosity events. To reconcile the observed results additional variables need to be added to the model of metastatic inefficiency. One possible variable that might explain the discrepancies is genetic background effects. Studies have demonstrated that the genetic background a tumor arises on can have significant affects on the ability of the tumor to metastasize and on gene expression profiles. Thus the observations could be reconciled by combining the theories, with genetic background influencing both metastatic efficiency and predictive gene expression profiles, upon which subsequently occur metastasis-promoting mutational and epigenetic events. If the genetic background is an important determinant of metastatic efficiency it would have significant implications for the clinical prediction and treatment of metastatic disease, as well as for the design of potential prevention strategies.

Gene expression profiling for prediction of clinical characteristics of breast cancer.

We have applied techniques of gene expression analysis to the analysis of human breast cancer by identifying metagene models with the capacity to discriminate breast tumors based on estrogen receptor (ER) status as well as the propensity for lymph node metastasis. We assess the utility and validity of these models in predicting status of tumors in cross-validation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications, based on the selection of gene subsets for each validation analysis. This latter point is of critical importance to the ability of applying these methodologies to clinical assessment of tumor phenotype. It is also clear from ER predictions that these analyses identify genes known to be involved in ER function but also identify new candidate genes involved in ER function. We believe these gene expression phenotypes have the potential to characterize the complex genetic alterations that typify the neoplastic state in a way that truly reflects the complexity of the regulatory pathways that are affected.